lugia 0.1.1

Universal schema converter for Python data types with optional dependencies

Lugia

Language Universal Gateway for Interoperable Adaptations

Universal schema converter for Python data types with optional dependencies.

Lugia provides bidirectional conversions between popular data schema types including PySpark, Polars, Pandas, Pydantic, dataclass, TypedDict, SQLModel, and SQLAlchemy. All dependencies are optional for maximum flexibility.

Features

• Universal Conversions: Convert between any supported schema type
• Optional Dependencies: Install only what you need
• Schema & Data Support: Convert both type definitions and data instances
• Type Detection: Automatic detection of source schema types
• Error Handling: Clear error messages for missing dependencies

Installation

Install the base package:

pip install lugia

Install with optional dependencies as needed:

# Install specific dependencies
pip install lugia[pydantic]
pip install lugia[pandas]
pip install lugia[polars]
pip install lugia[pyspark]
pip install lugia[sqlalchemy]
pip install lugia[sqlmodel]

# Install all dependencies
pip install lugia[all]

# Install with development dependencies
pip install lugia[dev]

Quick Start

Basic Usage

from lugia import convert, to_pydantic, to_pandas, to_polars
from pydantic import BaseModel

# Define a Pydantic model
class User(BaseModel):
    name: str
    age: int
    email: str

# Convert to dataclass
from lugia.dataclass import to_dataclass
UserDataclass = to_dataclass(User)

# Convert to Pandas DataFrame
import pandas as pd
user = User(name="John", age=30, email="[e-mail]")
df = to_pandas(user)
print(df)

Output:

   name  age             email
0  John   30  [e-mail]

Using the Unified Converter

from lugia import convert

# Convert Pydantic to dataclass
UserDataclass = convert(User, target="dataclass")

# Convert dataclass to Pydantic
UserPydantic = convert(UserDataclass, target="pydantic")

Supported Conversions

Pydantic

from lugia.pydantic import to_pydantic
from pydantic import BaseModel

class User(BaseModel):
    name: str
    age: int

# Convert from dataclass
import dataclasses

@dataclasses.dataclass
class UserDC:
    name: str
    age: int

UserPydantic = to_pydantic(UserDC)

# Convert from Pandas DataFrame
import pandas as pd
df = pd.DataFrame({"name": ["John"], "age": [30]})
UserPydantic = to_pydantic(df)

Dataclass

from lugia.dataclass import to_dataclass
from pydantic import BaseModel

class User(BaseModel):
    name: str
    age: int

# Convert to dataclass
UserDC = to_dataclass(User)

TypedDict

from lugia.typedict import to_typeddict
from typing import TypedDict
from pydantic import BaseModel

class User(BaseModel):
    name: str
    age: int

# Convert to TypedDict
UserTD = to_typeddict(User)

Pandas

from lugia.pandas import to_pandas, from_pandas
from pydantic import BaseModel

class User(BaseModel):
    name: str
    age: int

user = User(name="John", age=30)

# Convert to Pandas DataFrame
df = to_pandas(user)
print(df)

# Convert from Pandas to Pydantic
UserModel = from_pandas(df, target_type="pydantic")

Output:

   name  age
0  John   30

Polars

from lugia.polars import to_polars, from_polars
from pydantic import BaseModel

class User(BaseModel):
    name: str
    age: int

user = User(name="John", age=30)

# Convert to Polars DataFrame
df = to_polars(user)
print(df)

# Convert from Polars to Pydantic
UserModel = from_polars(df, target_type="pydantic")

Output:

shape: (1, 2)
┌──────┬─────┐
│ name ┆ age │
│ ---  ┆ --- │
│ str  ┆ i64 │
╞══════╪═════╡
│ John ┆ 30  │
└──────┴─────┘

PySpark

from lugia.pyspark import to_pyspark, from_pyspark
from pyspark.sql import SparkSession
from pydantic import BaseModel

class User(BaseModel):
    name: str
    age: int

spark = SparkSession.builder \
    .appName("test") \
    .master("local[1]") \
    .config("spark.driver.host", "localhost") \
    .config("spark.driver.bindAddress", "127.0.0.1") \
    .getOrCreate()

# Convert to PySpark StructType (schema)
struct_type = to_pyspark(User)
print(f"To PySpark StructType: {struct_type}")

# Convert to PySpark DataFrame (data)
user = User(name="John", age=30)
df = to_pyspark(user, spark_session=spark)
print("To PySpark DataFrame:")
df.show()

# Convert from PySpark to Pydantic
UserModel = from_pyspark(struct_type, target_type="pydantic")

spark.stop()

Output:

To PySpark StructType: StructType([StructField('name', StringType(), True), StructField('age', LongType(), True)])

To PySpark DataFrame:
+----+----+
|name| age|
+----+----+
|John|  30|
+----+----+

SQLAlchemy

from lugia.sqlalchemy import to_sqlalchemy, from_sqlalchemy
from sqlalchemy import MetaData
from pydantic import BaseModel

class User(BaseModel):
    name: str
    age: int

# Convert to SQLAlchemy Table
metadata = MetaData()
table = to_sqlalchemy(User, table_name="users", metadata=metadata)
print(f"To SQLAlchemy Table: {table}")
print(f"Columns: {[c.name for c in table.columns]}")

# Convert from SQLAlchemy to Pydantic
UserModel = from_sqlalchemy(table, target_type="pydantic")

Output:

To SQLAlchemy Table: users
Columns: ['name', 'age']

SQLModel

from lugia.sqlmodel import to_sqlmodel, from_sqlmodel
from pydantic import BaseModel

class User(BaseModel):
    name: str
    age: int

# Convert to SQLModel
UserSQLModel = to_sqlmodel(User)
print(f"To SQLModel: {UserSQLModel}")

# Convert from SQLModel to Pydantic
UserPydantic = from_sqlmodel(UserSQLModel, target_type="pydantic")

Output:

To SQLModel: <class 'abc.UserSQLModel'>

Type Detection

Lugia can automatically detect the type of your schema or data:

from lugia import detect_type
from pydantic import BaseModel

class User(BaseModel):
    name: str

# Detect type
print(detect_type(User))  # "pydantic"
print(detect_type(User(name="John")))  # "pydantic"

Output:

Type of class: pydantic
Type of instance: pydantic

Error Handling

When a required optional dependency is missing, Lugia raises a clear error:

from lugia.exceptions import MissingDependencyError
from lugia.pandas import to_pandas

try:
    df = to_pandas(some_data)
except MissingDependencyError as e:
    print(e)  # "Missing optional dependency 'pandas' required for Pandas conversions. Install it with: pip install lugia[pandas]"

API Reference

Core Functions

• convert(source, target, target_type): Unified conversion function
• detect_type(obj): Detect the type of a schema or data object

Conversion Functions

Each module provides conversion functions:

• lugia.pydantic.to_pydantic(source): Convert to Pydantic
• lugia.dataclass.to_dataclass(source): Convert to dataclass
• lugia.typedict.to_typeddict(source): Convert to TypedDict
• lugia.pandas.to_pandas(source): Convert to Pandas DataFrame
• lugia.pandas.from_pandas(df, target_type): Convert from Pandas
• lugia.polars.to_polars(source): Convert to Polars DataFrame/Schema
• lugia.polars.from_polars(polars_obj, target_type): Convert from Polars
• lugia.pyspark.to_pyspark(source, spark_session): Convert to PySpark
• lugia.pyspark.from_pyspark(pyspark_obj, target_type): Convert from PySpark
• lugia.sqlalchemy.to_sqlalchemy(source, table_name, metadata): Convert to SQLAlchemy
• lugia.sqlalchemy.from_sqlalchemy(sa_obj, target_type): Convert from SQLAlchemy
• lugia.sqlmodel.to_sqlmodel(source): Convert to SQLModel
• lugia.sqlmodel.from_sqlmodel(sqlmodel_class, target_type): Convert from SQLModel

Development

Setup

# Clone the repository
git clone https://github.com/eddiethedean/lugia.git
cd lugia

# Install with development dependencies
pip install -e ".[dev,all]"

Running Tests

pytest

Code Formatting

black lugia tests

License

MIT License - see LICENSE file for details.

Author

Odos Matthews

• Email: [e-mail]
• GitHub: @eddiethedean

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Acknowledgments

Inspired by articuno for the optional dependency pattern.